The world of digital photography is often impeded by red-eye artifacts. Existing red-eye detection techniques typically depend on a-priori image segmentation routine in an attempt to detect a face of a person. However, the detection rate generally scores as low as 50%. In some cases, over-segmentation results in false positives, yielding a poor success rate.
Current techniques may utilize the intervention of a user after the detection phase. Furthermore, these techniques may utilize a machine learning algorithm, which adds to computational overhead. To minimize this overhead, these algorithms adopt additional optimization techniques.